The escalating threat of malicious encryption activities requires the development of advanced detection mechanisms capable of identifying and mitigating ransomware attacks with high precision. The Dynamic Entropic Signature Analysis (DESA) framework introduces a novel approach that leverages entropy variations to detect unauthorized encryption processes in real-time. By monitoring entropy fluctuations, DESA effectively distinguishes between benign and malicious activities, thereby enhancing the accuracy of ransomware detection systems. The framework's adaptive computational divergence metrics allow for dynamic threshold adjustments, accommodating the evolving nature of ransomware behaviors and reducing false positive rates. Comprehensive evaluations demonstrate DESA's superior performance compared to traditional detection methods, with notable improvements in detection accuracy, precision, and recall. Additionally, DESA's low resource utilization ensures minimal impact on system performance, facilitating seamless integration into existing cybersecurity infrastructures. The insights gained from entropy variation analyses provide a deeper understanding of ransomware behaviors, reinforcing the validity of entropy monitoring as a reliable indicator of malicious activities. Through these contributions, DESA offers a robust and efficient mechanism for enhancing cybersecurity measures against the evolving threat of ransomware.